Error modeling method and device for prediction context of reversible image watermarking

ABSTRACT

The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a national stage application under 35 U.S.C. 371 ofPCT Application No. PCT/CN2018/107914, filed on 27 Sep. 2018, which PCTapplication claimed the benefit of Chinese Patent Application No.2018110712119, filed on 13 Sep. 2018, the entire disclosure of each ofwhich are hereby incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the technical field of reversibleimage watermarking and in particular to an error modeling method anddevice for prediction context of reversible image watermarking.

BACKGROUND

Reversible watermarking becomes the current research hotspot of thedigital watermarking technology. Compared with the traditional digitalwatermarking technology, reversible watermarking can completely restorethe original host information without distortion. Reversiblewatermarking has great research value and good application prospect,especially for application fields with high fidelity requirements on theoriginal host information, for example, application fields such asaerial photography information collection. When reversible imagewatermark embedding is performed on an image, the image needs to becompressed, which often requires a predictor. However, the redundancy inthe image cannot be completely removed by simply estimating the currentpixel value from the prediction context in the predictor. Most of theprediction algorithms made by predictors are linear, which caneffectively analyze the linear correlation redundancy between pixels,but cannot remove the non-linear correlation redundancy between pixels,such as texture redundancy.

SUMMARY

In order to overcome the shortcomings in the prior art, an objective ofthe present disclosure is to provide an error modeling method and devicefor prediction context of reversible image watermarking. By the errormodeling of the prediction context of a predictor to find the non-linearcorrelation between a current pixel and the prediction context thereof,the non-linear correlation redundancy between pixels can be effectivelyremoved.

To address the problem, the present disclosure uses the followingtechnical solution.

An error modeling method for prediction context of reversible imagewatermarking is provided, which comprises following steps:

S1: scanning an original image to obtain a current pixel x[i,j] andadjacent pixels surrounding the current pixel;

S2: constructing prediction context according to the current pixelx[i,j] and the adjacent pixels surrounding the current pixel, andestablishing a predictor based on omnidirectional context;

S3: self-adaptively error molding for the prediction context to obtain aself-adaptive error model; and

S4: feeding output data from the self-adaptive error model back to thepredictor to update and correct the prediction context, so as to correcta prediction value of the current pixel x[i,j].

Further, in the step S2, a predictor based on omnidirectional context isestablished, the formula for the predictor being:

${\overset{\Cap}{x}\left\lbrack {i,j} \right\rbrack} = {\frac{x_{n} + x_{w} + x_{e} + x_{s}}{4} = \frac{{x\left\lbrack {{i - 1},j} \right\rbrack} + {x\left\lbrack {i,j,{- 1}} \right\rbrack} + {x\left\lbrack {i,{j + 1}} \right\rbrack} + {x\left\lbrack {{i + 1},j} \right\rbrack}}{4}}$

where, {circumflex over (x)}[i,j] is the prediction value of the pixelx[i,j], x_(n) is a pixel located directly above the pixel x[i,j], x_(w)is a pixel located directly to the left of the pixel x[i,j], x_(e) is apixel located directly to the right of the pixel x[i,j], and x_(s) is apixel located directly below the pixel x[i,j].

Further, self-adaptively error molding the prediction context in thestep S3 comprises following steps:

S31: dividing the original image into four sub-images, the originalimage being I={x[i,j]|1≤H,1≤j≤W}, where H and W are the height and widthof the original image, the four sub-images being:

U ₁ ={u ₁[i,j]=x[2i,2j] |1≤i≤H′,1≤j≤W′}

U ₂ ={u ₂[i,j]=x[2i,2j+1] |1≤i≤H′,1≤j≤W′}

U ₃ ={u ₃[i,j]=x[2i+1,2j] |1≤i≤H′,1≤j≤W′}

U ₄ ={u ₄[i,j]=x[2i+1,2j+1] |1≤i≤H′,1≤j≤W′}

where, H′ and W′ are the height and width of the sub-image U₁, thesub-image U₂, the sub-image U₃ and the sub-image U₄, respectively, andH′≤H and W′≤W;

S32: quantifying the value of the prediction context;

S33: calculating, by the quantified prediction context, a predictionerror of pixels in the four sub-images, the prediction error beingobtained by:

e[i,j]=u[i,j]−û[i,j]

where, u[i,j] is the pixel in the sub-image, û[i, j] is the predictionvalue of the pixel in the sub-image, and e[i,j] is the prediction errorof the pixel in the sub-image; and

S34: establishing a self-adaptive error model according to theprediction error.

Further, the height H′ and the width W′ of the sub-image U₁, thesub-image U₂, the sub-image U₃ and the sub-image U₄ satisfy thefollowing condition, respectively:

H′=└(H−2)/2┘

W′=└(w−2)/2┘.

Further, in the step S4, output data from the self-adaptive error modelis fed back to the predictor to update and correct the predictioncontext, so as to correct a prediction value of the current pixelx[i,j], and the corrected prediction value {dot over (x)} of the currentpixel x[i,j] is obtained by:

{dot over (x)}={circumflex over (x)}+ē(d,t)

where, t is the parameter for the quantified predicated context, d isthe prediction error of the predictor, ē(d,t) is the error feedback fedback to the predictor by the self-adaptive error model, and {circumflexover (x)} is the prediction value of the current pixel x[i,j] beforecorrection.

A device for storing an error modeling method for prediction context ofreversible image watermarking, comprising a control module and a storagemedium used for storing control instructions, the control module beingconfigured to read the control instructions in the storage medium andexecute the following steps:

Q1: scanning an original image to obtain a current pixel x[i,j] andadjacent pixels surrounding the current pixel;

Q2: constructing prediction context according to the current pixelx[i,j] and the adjacent pixels surrounding the current pixel, andestablishing a predictor based on omnidirectional context;

Q3: self-adaptively error molding the prediction context to obtain aself-adaptive error model; and

Q4: feeding output data from the self-adaptive error model back to thepredictor to update and correct the prediction context, so as to correcta prediction value of the current pixel x[i,j].

Further, when the control module executes the step Q2, a predictor basedon omnidirectional context is established, the formula for the predictorbeing:

${\overset{\Cap}{x}\left\lbrack {i,j} \right\rbrack} = {\frac{x_{n} + x_{w} + x_{e} + x_{s}}{4} = \frac{{x\left\lbrack {{i - 1},j} \right\rbrack} + {x\left\lbrack {i,j,{- 1}} \right\rbrack} + {x\left\lbrack {i,{j + 1}} \right\rbrack} + {x\left\lbrack {{i + 1},j} \right\rbrack}}{4}}$

where, {circumflex over (x)}[i,j] is the prediction value of the pixelx[i,j], x_(n) is a pixel located directly above the pixel x[i,j], x_(w)is a pixel located directly to the left of the pixel x[i,j], x_(e) is apixel located directly to the right of the pixel x[i,j], and x_(s) is apixel located directly below the pixel x[i,j].

Further, when the control module executes the step Q3, self-adaptivelyerror molding the prediction context comprises following steps:

Q31: dividing the original image into four sub-images, the originalimage being I={x[i,j]|1≤i≤H,1≤j≤W}, where H and Ware the height andwidth of the original image, the four sub-images being:

U={u ₁[i,j]=x[2i,2j] |1≤i≤H′,1≤j≤W′}

U ₂ ={u ₂[i,j]=x[2i,2j+1] |1≤i≤H′,1≤j≤W′}

U ₃ ={u ₃[i,j]=x[2i+1,2j] |1≤i≤H′,1≤j≤W′}

U ₄ ={u ₄[i,j]=x[2i+1,2j+1] |1≤i≤H′,1≤j≤W′}

where, H′ and W′ are the height and width of the sub-image U₁, thesub-image U₂, the sub-image U₃ and the sub-image U₄, respectively, andH′≤H and W′≤W;

Q32: quantifying the value of the prediction context;

Q33: calculating, by the quantified prediction context, a predictionerror of pixels in the four sub-images, the prediction error beingobtained by:

e[i,j]=u[i,j]−û[i,j]

where, u[i,j] is the pixel in the sub-image, û[i,j] is the predictionvalue of the pixel in the sub-image, and e[i,j] is the prediction errorof the pixel in the sub-image; and

Q34: establishing a self-adaptive error model according to theprediction error.

Further, the height H′ and the width W′ of the sub-image U₁, thesub-image U₂, the sub-image U₃ and the sub-image U₄ satisfy thefollowing condition, respectively:

H′=└(H−2)/2┘

W′=└(W−2)/2┘.

Further, when the control module executes the step Q4, output data fromthe self-adaptive error model is fed back to the predictor to update andcorrect the prediction context, so as to correct a prediction value ofthe current pixel x[i,j], and the corrected prediction value {dot over(x)} of the current pixel x[i,j] is obtained by:

{dot over (x)}={circumflex over (x)}+d (d,t)

where, t is the parameter for the quantified predicated context, d isthe prediction error of the predictor, ē(d,t) is the error feedback fedback to the predictor by the self-adaptive error model, and {circumflexover (x)} is the prediction value of the current pixel x[i,j] beforecorrection.

The present disclosure has the following beneficial effect. In the errormodeling method and device for prediction context of reversible imagewatermarking, the non-linear correlation redundancy in the image cannotbe completely removed by simply estimating the current pixel value fromthe prediction context in the predictor, and therefore, first, apredictor based on omnidirectional context is established; then, theprediction context is self-adaptively error modeled to obtain aself-adaptive error model; and finally, output data from theself-adaptive error model is fed back to the predictor to update andcorrect the prediction context, so as to correct a prediction value of acurrent pixel x[i,j]. Since the non-linear correlation between thecurrent pixel and the prediction context thereof, i.e., the non-linearcorrelation redundancy between pixels can be found by the error modelingof the prediction context of the predictor, the non-linear correlationredundancy between the pixels can be effectively removed. Thus, theembeddable watermarking capacity can be increased.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be further described below with reference tothe accompanying drawings and specific embodiments.

FIG. 1 is the flowchart of an error modeling method according to thepresent disclosure; and

FIG. 2 is a schematic view of prediction context of the predictor.

DETAILED DESCRIPTION

With reference to FIGS. 1 and 2, an error modeling method for predictioncontext of reversible image watermarking is provided, which comprisesfollowing steps:

S1: scanning an original image to obtain a current pixel x[i,j] andadjacent pixels surrounding the current pixel;

S2: constructing prediction context according to the current pixelx[i,j] and the adjacent pixels surrounding the current pixel, andestablishing a predictor based on omnidirectional context;

S3: self-adaptively error molding the prediction context to obtain aself-adaptive error model; and

S4: feeding output data from the self-adaptive error model back to thepredictor to update and correct the prediction context, so as to correcta prediction value of the current pixel x[i,j].

Wherein, in the step S2, a predictor based on omnidirectional context isestablished, the formula for the predictor being:

${\overset{\Cap}{x}\left\lbrack {i,j} \right\rbrack} = {\frac{x_{n} + x_{w} + x_{e} + x_{s}}{4} = \frac{{x\left\lbrack {{i - 1},j} \right\rbrack} + {x\left\lbrack {i,j,{- 1}} \right\rbrack} + {x\left\lbrack {i,{j + 1}} \right\rbrack} + {x\left\lbrack {{i + 1},j} \right\rbrack}}{4}}$

where, {circumflex over (x)}[i,j] is the prediction value of the pixelx[i,j], x_(n) is a pixel located directly above the pixel x[i,j], x_(w)is a pixel located directly to the left of the pixel x[i,j], x_(e) is apixel located directly to the right of the pixel x[i,j], and x_(s) is apixel located directly below the pixel x[i,j].

Wherein, self-adaptively error molding the prediction context in thestep S3 comprises following steps:

S31: dividing the original image into four sub-images, the originalimage being I={x[i,j]|1≤i≤H,1≤j≤W}, where H and W are the height andwidth of the original image, the four sub-images being:

U ₁ ={u ₁[i,j]=x[2i,2j] |1≤i≤H′,1≤j≤W′}

U ₂ ={u ₂[i,j]=x[2i,2j+1] |1≤i≤H′,1≤j≤W′}

U ₃ ={u ₃[i,j]=x[2i+1,2j] |1≤i≤H′,1≤j≤W′}

U ₄ ={u ₄[i,j]=x[2i+1,2j+1] |1≤i≤H′,1≤j≤W′}

where, H′ and W′ are the height and width of the sub-image U₁, thesub-image U₂, the sub-image U₃ and the sub-image U₄, respectively, andH′ and W′ satisfy the following condition, respectively:

H′=└(H−2)/2┘

W′=└(w−2)/2┘;

S32: quantifying the value of the prediction context;

S33: calculating, by the quantified prediction context, a predictionerror of pixels in the four sub-images, the prediction error beingobtained by:

e[i,j]=u[i,j]-û[i,j]

where, u[i,j] is the pixel in the sub-image, û[i,j] is the predictionvalue of the pixel in the sub-image, and e[i,j] is the prediction errorof the pixel in the sub-image; and

S34: establishing a self-adaptive error model according to theprediction error.

Specifically, in the step S34, a self-adaptive error model isestablished according to the prediction error. The self-adaptive errormodel is a model commonly used in reversible image watermarking, and hasdifferent expression forms and may be established in different methodsaccording to different practical parameters. However, the error modelingmethod for prediction context of reversible image watermarking asdisclosed in the present disclosure is neither limited to the use of acertain specific self-adaptive error model, nor limited to a certainspecific method for establishing a self-adaptive error model. By theerror modeling method of the present disclosure, only output data fromthe self-adaptive error model is fed back to the predictor to update theprediction context of the predictor. Therefore, the specific method forestablishing the self-adaptive error model will not be repeated here.

Wherein, in the step S4, output data from the self-adaptive error modelis fed back to the predictor to update and correct the predictioncontext, so as to correct a prediction value of the current pixelx[i,j], and the corrected prediction value {dot over (x)} of the currentpixel x[i,j] is obtained by:

{dot over (x)}={circumflex over (x)}+ē(d,t)

[0075] where, t is the parameter for the quantified predicated context,d is the prediction error of the predictor, ē(d,t) is the error feedbackfed back to the predictor by the self-adaptive error model, and{circumflex over (x)} is the prediction value of the current pixelx[i,j] before correction.

Additionally, a device for storing an error modeling method forprediction context of reversible image watermarking is provided,comprising a control module and a storage medium used for storingcontrol instructions, the control module being configured to read thecontrol instructions in the storage medium and execute the followingsteps:

Q1: scanning an original image to obtain a current pixel x[i,j] andadjacent pixels surrounding the current pixel;

Q2: constructing prediction context according to the current pixelx[i,j] and the adjacent pixels surrounding the current pixel, andestablishing a predictor based on omnidirectional context;

Q3: self-adaptively error molding the prediction context to obtain aself-adaptive error model; and

Q4: feeding output data from the self-adaptive error model back to thepredictor to update and correct the prediction context, so as to correcta prediction value of the current pixel x[i,j].

Wherein, when the control module executes the step Q2, a predictor basedon omnidirectional context is established, the formula for the predictorbeing:

${\overset{\Cap}{x}\left\lbrack {i,j} \right\rbrack} = {\frac{x_{n} + x_{w} + x_{e} + x_{s}}{4} = \frac{{x\left\lbrack {{i - 1},j} \right\rbrack} + {x\left\lbrack {i,j,{- 1}} \right\rbrack} + {x\left\lbrack {i,{j + 1}} \right\rbrack} + {x\left\lbrack {{i + 1},j} \right\rbrack}}{4}}$

where, {circumflex over (x)}[i,j] is the prediction value of the pixelx[i,j], x_(n) is a pixel located directly above the pixel x[i,j], x_(w)is a pixel located directly to the left of the pixel x[i,j], x_(e) is apixel located directly to the right of the pixel x[i,j], and x_(s) is apixel located directly below the pixel x[i,j].

Wherein, when the control module executes the step Q3, self-adaptivelyerror molding the prediction context comprises following steps:

Q31: dividing the original image into four sub-images, the originalimage being I={x[i,j]|1≤i≤H,1≤j≤W}, where H and Ware the height andwidth of the original image, the four sub-images being:

U ₁ ={u ₁[i,j]=x[2i,2j] |1≤i≤H′,1≤j≤W′}

U ₂ ={u ₂[i,j]=x[2i,2j+1] |1≤i≤H′,1≤j≤W′}

U ₃ ={u ₃[i,j]=x[2i+1,2j] |1≤i≤H′,1≤j≤W′}

U ₄ ={u ₄[i,j]=x[2i+1,2j+1] |1≤i≤H′,1≤j≤W′}

where, H′ and W′ are the height and width of the sub-image U₁, thesub-image U₂, the sub-image U₃ and the sub-image U₄, respectively, andH′_H and W′sW;

Q32: quantifying the value of the prediction context;

Q33: calculating, by the quantified prediction context, a predictionerror of pixels in the four sub-images, the prediction error beingobtained by:

e[i,j]=u[i,j]-û[i,j]

where, u[i,j] is the pixel in the sub-image, û[i,j] is the predictionvalue of the pixel in the sub-image, and e[i,j] is the prediction errorof the pixel in the sub-image; and

Q34: establishing a self-adaptive error model according to theprediction error.

Wherein, the height H′ and the width W′ of the sub-image U₁, thesub-image U₂, the sub-image U₃ and the sub-image U₄ satisfy thefollowing condition, respectively:

H′=└(H−2)/2┘

W′=└(W−2)/2┘.

Wherein, when the control module executes the step Q4, output data fromthe self-adaptive error model is fed back to the predictor to update andcorrect the prediction context, so as to correct a prediction value ofthe current pixel x[i,j], and the corrected prediction value {dot over(x)} of the current pixel x[i,j] is obtained by:

{dot over (x)}={circumflex over (x)}+ē(d,t)

where, t is the parameter for the quantified predicated context, d isthe prediction error of the predictor, ē(d,t) is the error feedback fedback to the predictor by the self-adaptive error model, and {circumflexover (x)} is the prediction value of the current pixel x[i,j] beforecorrection.

Specifically, the non-linear correlation redundancy in the image cannotbe completely removed by simply estimating the current pixel value fromthe prediction context in the predictor, and therefore, first, apredictor based on omnidirectional context is established; then, theprediction context is self-adaptively error modeled to obtain aself-adaptive error model; and finally, output data from theself-adaptive error model is fed back to the predictor to update andcorrect the prediction context, so as to correct a prediction value of acurrent pixel x[i,j]. Since the non-linear correlation between thecurrent pixel and the prediction context thereof, i.e., the non-linearcorrelation redundancy between pixels can be found by the error modelingof the prediction context of the predictor, the non-linear correlationredundancy between the pixels can be effectively removed. Thus, theembeddable watermarking capacity can be increased.

For half-directional predictors or omnidirectional predictors, theprediction algorithm is mostly linear. Such a linear algorithm caneffectively analyze the linear correlation redundancy between pixels,but fails to remove the non-linear correlation redundancy between thepixels, such as texture redundancy. However, by modeling the predictioncontext of the predictor, the non-linear correlation between the currentpixel and the prediction context thereof can be found. Since thepredictor used in this embodiment is a predictor based onomnidirectional context, the prediction context of the predictor iscomposed of at least eight pixels surrounding the current pixel, andeach pixel has a value between 0 and 255. If the prediction context ofthe predictor is directly modeled, the model will have 8×256 cases,which will lead to a large amount of calculation and thus reduce thecalculation efficiency. Therefore, the value of the prediction contextis first quantified, and then the self-adaptive error modeling isperformed using the quantified prediction context. In addition, becausethere is a certain correlation between prediction errors, the use ofself-adaptive error modeling can also effectively eliminate theprediction bias of the predictor, thereby improving the predictionaccuracy of the predictor. Then, the output data from the self-adaptiveerror model is fed back to the predictor, and the prediction context isupdated and corrected, so as to correct the prediction value of thecurrent pixel x[i,j]. Since the corrected prediction value can reducethe prediction error of the predictor, the accuracy of prediction can beenhanced. When quantifying the value of the prediction context, assumingthat the parameter for the quantified prediction context is t and theprediction error of the predictor is d, then the error feedback ē(d,t)can be obtained. Then, the error feedback ē(d,t) is used to correct thepredictor, and the current pixel x[i,j] after introducing the correctedprediction value will become {dot over (x)} from {circumflex over (x)},that is, {dot over (x)}={circumflex over (x)}+ē(d,t). In this case, thecorrected {dot over (x)} will be closer to x than {circumflex over (x)}.Therefore, the prediction error will be smaller, which can effectivelyincrease the embeddable watermarking capacity.

The preferred embodiments of the present disclosure have beenspecifically described. However, the present disclosure is not limitedto those implementations. A person of ordinary skill in the art may makevarious equivalent variations or replacements without departing from thespirit of the present disclosure, and those equivalent variations orreplacements shall be included in the scope defined by the appendedclaims.

1. An error modeling method for prediction context of reversible imagewatermarking, comprising following steps: S1: scanning an original imageto obtain a current pixel x[i,j] and adjacent pixels surrounding thecurrent pixel; S2: constructing prediction context according to thecurrent pixel x[i,j] and the adjacent pixels surrounding the currentpixel, and establishing a predictor based on omnidirectional context;S3: self-adaptively error molding for the prediction context to obtain aself-adaptive error model; and S4: feeding output data from theself-adaptive error model back to the predictor to update and correctthe prediction context, so as to correct a prediction value of thecurrent pixel x[i,j].
 2. The error modeling method for predictioncontext of reversible image watermarking of claim 1, wherein, in thestep S2, the predictor based on omnidirectional context is established,the formula for the predictor being:${\overset{\Cap}{x}\left\lbrack {i,j} \right\rbrack} = {\frac{x_{n} + x_{w} + x_{e} + x_{s}}{4} = \frac{{x\left\lbrack {{i - 1},j} \right\rbrack} + {x\left\lbrack {i,j,{- 1}} \right\rbrack} + {x\left\lbrack {i,{j + 1}} \right\rbrack} + {x\left\lbrack {{i + 1},j} \right\rbrack}}{4}}$where, {circumflex over (x)}[i, j] is the prediction value of the pixelx[i,j], x_(n) is a pixel located directly above the pixel x[i,j], x_(w)is a pixel located directly to the left of the pixel x[i,j], x_(e) is apixel located directly to the right of the pixel x[i,j], and x_(s) is apixel located directly below the pixel x[i,j].
 3. The error modelingmethod for prediction context of reversible image watermarking of claim2, wherein self-adaptively error molding the prediction context in thestep S3 comprises following steps: S31: dividing the original image intofour sub-images, the original image being I={x[i, j]|1≤i≤H,1≤j≤W}, whereH and W are the height and width of the original image, the foursub-images being:U ₁ ={u ₁[i,j]=x[2i,2j] |1≤i≤H′,1≤j≤W′}U ₂ ={u ₂[i,j]=x[2i,2j+1] |1≤i≤H′,1≤j≤W′}U ₃ ={u ₃[i,j]=x[2i+1,2j] |1≤i≤H′,1≤j≤W′}U ₄ ={u ₄[i,j]=x[2i+1,2j+1] |1≤i≤H′,1≤j≤W′} where, H′ and W′ are theheight and width of the sub-image U₁, the sub-image U₂, the sub-image U₃and the sub-image U₄, respectively, and H′≤H and W′≤W; S32: quantifyingthe value of the prediction context; S33: calculating, by the quantifiedprediction context, a prediction error of pixels in the four sub-images,the prediction error being obtained by:e[i,j]=u[i,j]−û[i,j] where, u[i,j] is the pixel in the sub-image, û[i,j] is the prediction value of the pixel in the sub-image, and e[i,j] isthe prediction error of the pixel in the sub-image; and S34:establishing a self-adaptive error model according to the predictionerror.
 4. The error modeling method for prediction context of reversibleimage watermarking of claim 3, wherein the height H′ and the width W′ ofthe sub-image U₁, the sub-image U₂, the sub-image U₃ and the sub-imageU₄ satisfy the following condition, respectively:H′=└(H−2)/2┘W′=└(w−2)/2┘.
 5. The error modeling method for prediction context ofreversible image watermarking of claim 3, wherein, in the step S4,output data from the self-adaptive error model is fed back to thepredictor to update and correct the prediction context, so as to correcta prediction value of the current pixel x[i,j], and the correctedprediction value {dot over (x)} of the current pixel x[i,j] is obtainedby:{dot over (x)}={circumflex over (x)}+ē(d,t) where, t is the parameterfor the quantified predicated context, d is the prediction error of thepredictor, ē(d,t) is the error feedback fed back to the predictor by theself-adaptive error model, and {circumflex over (x)} is the predictionvalue of the current pixel x[i,j] before correction.
 6. A device forstoring an error modeling method for prediction context of reversibleimage watermarking, comprising a control module and a storage mediumused for storing control instructions, the control module beingconfigured to read the control instructions in the storage medium andexecute the following steps: Q1: scanning an original image to obtain acurrent pixel x[i,j] and adjacent pixels surrounding the current pixel;Q2: constructing prediction context according to the current pixelx[i,j] and the adjacent pixels surrounding the current pixel, andestablishing a predictor based on omnidirectional context; Q3:self-adaptively error molding for the prediction context to obtain aself-adaptive error model; and Q4: feeding output data from theself-adaptive error model back to the predictor to update and correctthe prediction context, so as to correct a prediction value of thecurrent pixel x[i,j].
 7. The device according to claim 6, wherein, whenthe control module executes the step Q2, a predictor based onomnidirectional context is established, the formula for the predictorbeing:${\overset{\Cap}{x}\left\lbrack {i,j} \right\rbrack} = {\frac{x_{n} + x_{w} + x_{e} + x_{s}}{4} = \frac{{x\left\lbrack {{i - 1},j} \right\rbrack} + {x\left\lbrack {i,j,{- 1}} \right\rbrack} + {x\left\lbrack {i,{j + 1}} \right\rbrack} + {x\left\lbrack {{i + 1},j} \right\rbrack}}{4}}$where, {circumflex over (x)}[i, j] is the prediction value of the pixelx[i,j], x_(n) is a pixel located directly above the pixel x[i,j], x_(w)is a pixel located directly to the left of the pixel x[i,j], x_(e) is apixel located directly to the right of the pixel x[i,j], and x_(s) is apixel located directly below the pixel x[i,j].
 8. The device accordingto claim 7, wherein, when the control module executes the step Q3,self-adaptively error molding the prediction context comprises followingsteps: Q31: dividing the original image into four sub-images, theoriginal image being I={x[i,j]|1≤i≤H,1≤j≤W}, where H and W are theheight and width of the original image, the four sub-images being:U ₁ ={u ₁[i,j]=x[2i,2j] |1≤i≤H′,1≤j≤W′}U ₂ ={u ₂[i,j]=x[2i,2j+1] |1≤i≤H′,1≤j≤W′}U ₃ ={u ₃[i,j]=x[2i+1,2j] |1≤i≤H′,1≤j≤W′}U ₄ ={u ₄[i,j]=x[2i+1,2j+1] |1≤i≤H′,1≤j≤W′} where, H′ and W′ are theheight and width of the sub-image U₁, the sub-image U₂, the sub-image U₃and the sub-image U₄, respectively, and H′≤H and W′≤W; Q32: quantifyingthe value of the prediction context; Q33: calculating, by the quantifiedprediction context, a prediction error of pixels in the four sub-images,the prediction error being obtained by:e[i,j]=u[i,j]−û[i,j] where, u[i,j] is the pixel in the sub-image, û[i,j] is the prediction value of the pixel in the sub-image, and e[i,j] isthe prediction error of the pixel in the sub-image; and Q34:establishing a self-adaptive error model according to the predictionerror.
 9. The device according to claim 8, wherein the height H′ and thewidth W′ of the sub-image U₁, the sub-image U₂, the sub-image U₃ and thesub-image U₄ satisfy the following condition, respectively:H′=└(H−2)/2┘W′=└(W−2)/2┘.
 10. The device according to claim 8, wherein, when thecontrol module executes the step Q4, output data from the self-adaptiveerror model is fed back to the predictor to update and correct theprediction context, so as to correct a prediction value of the currentpixel x[i,j], and the corrected prediction value {dot over (x)} of thecurrent pixel x[i,j] is obtained by:{dot over (x)}={circumflex over (x)}+ē(d,t) where, t is the parameterfor the quantified predicated context, d is the prediction error of thepredictor, ē(d,t) is the error feedback fed back to the predictor by theself-adaptive error model, and {circumflex over (x)} is the predictionvalue of the current pixel x[i,j] before correction.